Skip to content
Wholesale and retail

Data-empowered digital commerce

To stand out in the global competition, wholesale and retail companies need to tap into process and customer data and think about the value they can extract from analytics. 

We can help
Manufacturing industries

Customer-centric, data-led manufacturing

For manufacturers, true digital transformation starts by envisioning where in the value chain can data be applied to make a difference.

We can help
Public Sector

Accessible and intuitive public services

All public sector services should be designed to serve citizens first. Digital solutions and applications must be easy to use, despite physical and cognitive disabilities.

We can help
Energy and utilities

Dynamic and resilient energy economy

Digital processes and data-led services help energy and utility sector companies develop a stable energy offering with transparent, customer-centric services.

We can help
Digital platform economy

Fast-tracking innovative business models

Shared platforms offer fast entry to new markets, cost-efficiently and scalably. But lasting value add comes from cross-industry collaboration and linking products with complimentary services.

We can help
Banking, finance and insurance

Agile business, customer-centric services

Digitally disrupted, the companies in the banking, finance and insurance sector must actively innovate new approaches to build omnichannel customer experiences that fully utilize data.

We can help
Medical devices manufacturers

User-centric healthcare applications

While medical device software is strictly regulated, there's room for innovations that make life easier for patients and caregivers. Stable and secure data flow is a must.

We can help

Blog: 7 practical ways that AI will impact continuous software development

Jonne Sjöholm

September 14 2023

AI is probably not out to take our jobs, but it’s about to make our jobs easier. This is true for a wide range of occupations, including software developers – tools like ChatGPT and GitHub copilot can already generate code. In this post, we’re going to explore potential scenarios and applications of generative AI in a future context, illustrating its prospective benefits. 

Here’s how AI will be able to help your development work:

Code generation

Developers often face complex problems that required writing lengthy pieces of code. With a generative AI tool, developers will be able to provide an outline of what they want the code to accomplish. The AI system, trained on thousands of lines of code across multiple languages, will then generate a detailed code structure, thereby saving considerable time and effort.

Bug identification

Debugging is a routine, but time-consuming, part of every developer's job. Generative AI will be able to sift through thousands of lines of code to identify potential errors and anomalies that could lead to bugs. This not only saves time, but also improves the overall quality and reliability of software.

Code refactoring

Maintaining and improving existing code is an essential but often tedious task. AI will be able to take original code as input and generate a more efficient and readable version, which helps maintain high coding standards and make the code easier for others to understand and work with.


Developers often create new APIs for their clients, and documenting these is a critical part of the job. AI tools can generate detailed, clear, and comprehensive documentation based on the API's code, thereby considerably reducing a developer’s workload and allowing them to focus on more complex tasks.

Helpful brainstorming tool

In addition to these direct applications within the coding process, generative AI can also be an incredibly useful brainstorming tool. Tools like ChatGPT have the potential to spark initial ideas, and might prove instrumental during the early stages of project ideation. 

Creating tests 

The benefits of AI tools like ChatGPT extended even further. They can streamline the development process, particularly when it comes to creating tests. Automating this process not only saves precious time but will also allow developers to focus on the more intricate aspects of their projects.

Facilitator of learning and understanding

AI tools already have an uncanny knack for simplifying complex concepts, making them easier to understand. With AI as a co-contributor, learning under pressure will become a more enjoyable exercise.

Some things to keep in mind about generative AI

Bear in mind that the capabilities discussed are largely hypothetical at this stage, as they entail further development and refinement. Tools like ChatGPT can sometimes generate inaccurate or outdated information. 

This means there is a constant need for verification. Any code or suggestions provided by ChatGPT or similar tools have to be checked thoroughly, either by a colleague or a reliable source online. This is especially critical for inexperienced developers who might be tempted to implement AI-generated suggestions without fully understanding them.

MoreoverAI tools aren’t immune to misunderstanding queries or providing irrelevant answers. In addition, there is a need to scrutinize the output of AI tools to avoid introducing security vulnerabilities or performance issues into the code. Users needed to request code in small chunks and seek out examples to ensure its quality and safety.

Like any tool, generative AI has its strengths and weaknesses – but when used wisely, it will unlock new levels of efficiency and productivity in the ever-evolving world of tech.

Did we catch your interest?

Drop us a message, we'll be happy to discuss with you!